Vitellogenin receptors (VgRs) play crucial roles in oogenesis by mediating endocytosis of vitellogenin and other nutrients in ovipara. We conducted small RNA sequencing and screening with a luciferase reporter system, and found that bmo-miR-2739 and a novel miRNA (novel-miR-167) coordinately regulate the expression of VgR in Bombyx mori (BmVgR). Further analyses suggested that these two miRNAs direct target repression by binding directly to the BmVgR 3′ untranslated region. Forced expression of either miRNA using the piggyBac system blocked vitellogenin (Vg) transport and retarded ovariole development. Antagomir silencing of bmo-miR-2739 or novel-miR-167 resulted in increased amounts of BmVgR protein in the ovaries and BmVgR mRNA in the fat body. This evidence, combined with spatiotemporal expression profiles, revealed that these two miRNAs function together to fine-tune the amount of BmVgR protein for ovarian development. Additionally, novel-miR-167 was mainly responsible for the post-transcriptional repression of BmVgR in non-ovarian tissues. The results of this study contribute to our understanding of the function of miRNAs during ovarian development of a lepidopteran and suggest a new strategy for controlling insect reproduction.

Cell clustering is typically the initial step in single-cell RNA sequencing (scRNA-seq) analyses. The performance of clustering considerably impacts the validity and reproducibility of cell identification. A variety of clustering algorithms have been developed for scRNA-seq data. These algorithms generate cell label sets that assign each cell to a cluster. However, different algorithms usually yield different label sets, which can introduce variations in cell-type identification based on the generated label sets. Currently, the performance of these algorithms has not been systematically evaluated in single-cell transcriptome studies. Herein, we performed a critical assessment of seven state-of-the-art clustering algorithms including four deep learning-based clustering algorithms and commonly used methods Seurat, Cosine-based Tanimoto similarity-refined graph for community detection using Leiden’s algorithm (CosTaL) and Single-cell consensus clustering (SC3). We used diverse evaluation indices based on 10 different scRNA-seq benchmarks to systematically evaluate their clustering performance. Our results show that CosTaL, Seurat, Deep Embedding for Single-cell Clustering (DESC) and SC3 consistently outperformed Single-Cell Clustering Assessment Framework and scDeepCluster based on nine effectiveness scores. Notably, CosTaL and DESC demonstrated superior performance in clustering specific cell types. The performance of the single-cell Variational Inference tools varied across different datasets, suggesting its sensitivity to certain dataset characteristics. Notably, DESC exhibited promising results for cell subtype identification and capturing cellular heterogeneity. In addition, SC3 requires more memory and exhibits slower computation speed compared to other algorithms for the same dataset. In sum, this study provides useful guidance for selecting appropriate clustering methods in scRNA-seq data analysis.

Vitellogenin receptors (VgRs) play critical roles in egg formation by transporting vitellogenin (Vg) into oocytes in insects. Although the function of VgR in insects is well studied, the transcriptional regulation of this gene is still unclear. Here, we cloned the promoter of the VgR gene from Bombyx mori (BmVgR), and predicted many POU cis-response elements (CREs) in its promoter. Electrophoretic mobility shift and chromatin immunoprecipitation assays showed that the POU transcription factor POU-M2 bound directly to the CREs of the promoter. Overexpression of POU-M2 in an ovarian cell line (BmNs) enhanced BmVgR transcription and promoter activity detected by quantitative reverse transcription PCR and luciferase reporter assays. Analyses of expression patterns indicated that POU-M2 was expressed in ovary at day two of wandering stage initially, followed by BmVgR. RNA interference of POU-M2 significantly reduced the transcription of BmVgR in ovary and egg-laying rate. Our results suggest a novel function for the POU factor in silkworm oogenesis by its involvement in BmVgR regulation and expands the understanding of POU factors in insect VgR expression.